Create app.py

app.py

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+import os
+
+os.environ["ANTHROPIC_API_KEY"] = "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"
+import gradio as gr
+from langchain.memory import ConversationBufferMemory
+from langchain.chains import RetrievalQA
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.vectorstores import Chroma
+from langchain.chat_models import ChatAnthropic
+
+model_kwargs = {'trust_remote_code': True}
+embeddings = HuggingFaceEmbeddings(model_name="jinaai/jina-embeddings-v2-base-en",
+                                   model_kwargs=model_kwargs)
+llm = ChatAnthropic(model='claude-2',
+                    temperature=0)
+
+# Load the persisted Chroma database
+persist_directory='/Electric_Machinerydb'
+embeddings = embeddings
+chroma_db = Chroma(persist_directory=persist_directory, embedding_function=embeddings)
+retriever = chroma_db.as_retriever(search_kwargs={"k": 3}, return_source_documents=True)
+
+# Initialize ConversationBufferMemory
+memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True, input_key="query", output_key="result")
+
+# Create a question-answering chain using the retriever
+qa_chain = RetrievalQA.from_chain_type(
+    llm=llm,
+    chain_type="stuff",
+    retriever=retriever,
+    return_source_documents=True,
+    memory=memory,
+    output_key="result"  # Specify the output key
+)
+
+# Gradio app
+def ask_question(question, chat_history):
+    query = question.strip()
+    if query:
+        result = qa_chain({"query": query})
+        answer = result['result']
+        chat_history.append((query, answer))
+        metadata = show_metadata(chat_history)
+        return "", chat_history, metadata
+    else:
+        chat_history.append(("", "Please enter a question."))
+        return "", chat_history, ""
+
+def show_metadata(chat_history):
+    if chat_history:
+        query, answer = chat_history[-1]
+        result = qa_chain({"query": query})
+        metadata = ""
+        for doc in result['source_documents']:
+            metadata += f"Page: {doc.metadata['page']}\n"
+            metadata += f"Source: {doc.metadata['source']}\n"
+            metadata += f"Content: {doc.page_content}\n"
+            metadata += "---\n"
+        return metadata
+    return ""
+
+def clean_history():
+    memory.clear()
+    return [], "", ""
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Electric Machinery QA by Tamil")
+
+    with gr.Row():
+        with gr.Column(scale=2):
+            question = gr.Textbox(label="Question")
+            ask_btn = gr.Button("Ask")
+            clean_btn = gr.Button("Clean")
+            chatbot = gr.Chatbot(label="Conversation")
+
+        with gr.Column(scale=1):
+            gr.Markdown("## Metadata")
+            metadata_output = gr.Textbox(label="Source Information", lines=10)
+
+    ask_btn.click(ask_question, inputs=[question, chatbot], outputs=[question, chatbot, metadata_output])
+    clean_btn.click(clean_history, inputs=[], outputs=[chatbot, question, metadata_output])
+
+demo.launch()